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Activity Number: 177 - Section on Statistical Learning and Data Science CPapers 2
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #329928
Title: Coordinate-Independent Sparse Estimation in Semiparametric Models
Author(s): Haileab Hilafu* and Sandra Safo
Companies: University of Tennessee and University of Minnesota
Keywords: Coordinate-Independent Estimation; Dimension Reduction; Semi-parametric Models

A number of dimension reduction methods for semi-parametric multi-index models can be formulated as a generalized eigenvalue decomposition problem. For simultaneous dimension reduction and variable selection, sparse estimation of the eigenvectors that span the dimension reduction subspace is often obtained for each of the directions independently. However, such sparse estimation approaches do not yield estimates that are invariant to orthogonal transformation of the basis matrix that represents the dimension reduction subspace. We exploit a group-dantzig type formulation to obtain coordinate-independent sparse estimates that are invariant under orthogonal transformation of the dimension reduction subspace. Extensive simulation and real data application will be presented to demonstrate the effectiveness of the proposed method and compare its performance with other competing methods. Consistency of the estimator is also established.

Authors who are presenting talks have a * after their name.

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